AI Security: 10 Top Risks and Best Practices in 2026

Technology businesses are officially in the age of AI development and implementation, as they continue to build multi-agent systems and see higher ROI from AI projects. Yet, with increased innovation comes greater risk. That includes AI security challenges like adversarial machine learning. According to the DigitalOcean February 2026 Currents Research Report, 34% of respondents have challenges managing security across their AI tools. This is no small number, especially as the attack vectors for AI are growing to include AI data, models, outputs, and deepfake content.

Threats such as prompt injections, LLM poisoning, model inversion, and adversarial inputs can manipulate outputs, leak sensitive information, or degrade system performance in ways that are difficult to detect with conventional security tooling. For technical decision-makers, this means AI security is no longer just about infrastructure hardening—it requires a deeper understanding of how models behave in real-world conditions. Addressing these attacks requires best practices, including standardized frameworks to help protect data and assist in guarding against malicious models.

What is AI security?

AI security is the practice of protecting AI systems from outside threats alongside proactively maintaining overall data and model health. This differs from more traditional IT security, as the main attack surface lies in models and datasets rather than the underlying supporting infrastructure (such as servers, networks, and endpoints). This requires developers to adopt more up-to-date security strategies to proactively audit data hygiene and assess model security. Associated risks that AI security prevents include attacks on AI data, model theft, exposure of sensitive data, and the spread of misinformation through deepfakes or other AI-generated content.

AI compliance and security standards

As AI security risks increase over time and more data is used in AI models across industries, more standards have emerged to govern AI data sovereignty and use. They include:

• General Data Protection Regulation (GDPR): Effective in 2018, this data privacy regulation covers the processing of personal data by companies and individuals within the European Union (EU). For data operations in the EU, individuals have a right to know what data is being collected about them, how it is being used, and to control that use. This means that all AI data collected from EU citizens or used for AI use cases within the EU must adhere to the GDPR regulation, including data anonymization, data governance, purpose specification, and user education.

• EU AI Act: This regulation, which went live in August 2024, serves as a legal framework for AI use within the EU. It is not designed to regulate individual use, but rather organizations that use AI. It classifies AI use under four levels: unacceptable, high, limited, and minimal. There are also transparency requirements for general-purpose AI, which require organizations to establish policies that comply with EU copyright laws and to make data training sets for models publicly available. For high-risk organizations, companies must strive to have these systems comply with the act’s requirements, maintain quality management systems, maintain system logs, and implement rigorous data governance measures.

• U.S. Securities and Exchange Commission (SEC): In the United States, the SEC launched an AI Task Force in 2025 to promote the responsible use of AI, remove barriers to progress, and centralize the SEC’s efforts to handle the overall AI lifecycle. This effort primarily evaluates how AI is used within the SEC, its effectiveness, and its potential benefits to government agencies.

• U.S.-state-level laws: Across the U.S., multiple states have enacted laws regarding the use of AI technology and content, as well as consumer protections. AI laws vary in scope but primarily focus on limiting the use of AI in company advertising, requiring disclosures about AI chatbot data use, prohibiting AI-based decision-making in specific industries, and banning unauthorized deepfakes.

AI security frameworks

When it comes to more technical frameworks, there are standards from the National Institute of Standards and Technology (NIST), Open Worldwide Application Security Project (OWASP), International Organization for Standardization ISO, and The MITRE Corporation.

Here’s how these organizations work to regulate AI security and their primary focus areas:

10 AI security threats to track in 2026

AI technologies come with a large attack surface that includes training data, inference data, model architectures, and AI-generated content. This means that developers must be aware of the main types of AI security attacks to effectively develop security measures and response plans. Here are 10 types of AI security risks to keep in mind:

1. Multi-modal and indirect prompt injection attacks

Multi-modal and indirect prompt injection attacks remain prevalent in 2026, evolving from simple text jailbreaking to complex vectors that subvert agentic execution loops. This exploit relies on a parsing problem: AI’s difficulty in potentially distinguishing between system instructions and untrusted user input processed in the same sequence. This risk has moved beyond inappropriate chatbot responses to unauthorized tool calls and data exfiltration from integrated services like CRMs. Indirect versions are particularly dangerous, as they turn trusted sources—such as retrieved web pages or documents—into hidden attack vectors. Technically, adversaries can use typographic strategies or adversarial disruptions to embed instructions within images or audio, bypassing text-based filters. In August 2025, Google Jules was discovered to be vulnerable to invisible prompt injection attacks involving hidden Unicode characters, allowing attackers to plant invisible instructions in a GitHub issue and run backdoor code or arbitrary commands.

2. Training data and model poisoning

Data poisoning involves feeding malicious data into training sets, fine-tuning datasets, or retrieval augmented generation (RAG) knowledge bases to compromise a model’s foundational reliability before deployment. This sophisticated supply chain threat creates latent vulnerabilities that persist even after retraining and can lead to biased decisions or systemic failures. A poisoned model might perform normally on the majority of inputs, but trigger a specific malicious output when presented with a rare, attacker-controlled pattern. The technical mechanism involves injecting crafted examples, such as specific pixel patterns or biased text sequences, to introduce a hidden backdoor. These attacks are often pre-planned and can be activated at any time, like an AI sleeper agent.

3. Model inversion and data restructuring

Model inversion is a privacy attack where adversaries use a model’s outputs to reverse-engineer sensitive information about its training data. Attackers typically access the model’s API and submit a large volume of carefully crafted queries to observe the model’s prediction confidence scores. By analyzing these patterns, they use their own attack to iteratively reconstruct personal attributes such as names, addresses, and images. This allows confidential data to be extracted without ever requiring a direct breach of the underlying database.

4. Membership inference attacks

Membership inference attacks (MIAs) aim to determine whether specific data points were included in a target model’s training set, posing a severe privacy threat. These attacks have become a formidable tool for de-identifying sensitive training samples. The primary risk is the disclosure of an individual’s participation in a sensitive dataset, which could reveal medical diagnoses or other regulated personal information. These attacks exploit the model’s tendency to exhibit lower loss and higher confidence on samples it has already seen during training. Attackers build shadow models on public data to learn these confidence thresholds and then query the production endpoint with target samples to identify members. Doing so challenges traditional concepts of anonymization and may expose you to significant legal liability.

5. Model theft and extraction

Model theft and extraction involves building a near-perfect replica of a proprietary AI model by systematically querying its API and recording the outputs. As organizations increasingly deliver LLM-as-a-Service, this provides a high volume of input-output pairs for adversaries to harvest. The organizational risk is the theft of expensive intellectual property, allowing competitors to duplicate a model’s logic at a fraction of the cost. To achieve this, the attacker sends crafted inputs across the entire input space to build a comprehensive dataset of responses. Then they use distillation techniques to train a surrogate model that inherits the original’s specialized behavior, failure modes, and biases. This results in a loss of competitive differentiation even if the organization’s core infrastructure remains uncompromised. A common way to do this is via LLMjacking, the sale of stolen API credentials for unauthorized access, to either add malicious code or steal sensitive information.

6. Memory poisoning

Memory poisoning targets AI agents that use long-term persistent storage to maintain context across multiple user sessions. Unlike transient prompt injections, this attack involves implanting false information into an agent’s memory that persists long after the initial interaction. An attacker might submit a support ticket asking an agent to remember a fraudulent rule, such as routing invoices to a new address. The agent recalls this malicious context in future sessions, leading to unauthorized actions that bypass traditional monitoring systems. This alters the AI’s logic to serve an attacker’s future intent.

7. Cascading failures in multi-agent workflows

Cascading failures occur in multi-agent workflows when a compromise in one specialized agent propagates through the entire automated infrastructure. Many architectures will rely on orchestration agents managing multiple downstream agents for tasks such as vendor verification or payments. If the initial agent returns false data, the error rapidly spreads because downstream agents assume it comes from a trusted source. This machine-speed collapse is difficult to contain because the chain of reasoning in these systems is often opaque to human observers. Consequently, a single point of failure can lead to systemic subversion of an organization’s entire automated operational pipeline. Last month, Meta’s AI agent posted unauthorized advice on an internal employee forum, prompting a second agent to run commands and expose internal employee data for over 2 hours.

8. AI supply chain compromise

The 2026 AI supply chain includes pre-trained models, datasets, and third-party libraries that introduce hidden threats through public repositories. Attackers exploit the trust relationship between developers and registries by uploading models with embedded malware. These models use bytecode injection to hide malicious payloads within model weights, which are executed automatically when the model is loaded. Some attacks even use model confusion to trick developers into downloading malicious replacements for common dependencies. This means that an organization downloading a popular model can inherit a full system compromise before any AI inference even begins.

9. Evasion attacks and adversarial examples

Evasion attacks involve making subtle, often imperceptible changes to input data to cause an AI model to misclassify or misinterpret information at inference time. These attacks pose a severe physical risk to AI use cases, such as autonomous vehicles being tricked into ignoring stop signs by small stickers. In enterprise settings, evasion can be used to bypass facial recognition systems, fraud detection, or AI-based malware scanners. Attackers typically use gradient-based methods, like the Fast Gradient Sign Method (FGSM), to push data across the model’s decision boundary with high confidence. These adversarial examples exploit the fact that models rely on statistical patterns rather than true semantic understanding.

10. AI-driven deepfakes and identity subversion

Deepfakes involve the use of generative AI to create hyper-realistic synthetic media, including audio, video, and images, designed to deceive human observers. This type of attack has become a primary vector for businesses, where attackers impersonate executives to authorize fraudulent transfers. Technically, the risk is driven by the ease of access to high-fidelity models that can clone a voice or a face from just a few seconds of publicly available data. These attacks exploit the inherent human trust in sensory input, often bypassing traditional security awareness training that focuses on text-based phishing. An example of this is the Grok userbase bypassing the AI’s guardrails to generate deepfakes and explicit images, despite the terms of use, causing unauthorized creation of AI content.

AI security best practices for proactive measures

Part of catching AI security threats is implementing best practices within your organization to prevent security incidents before they happen. These include regular steps that can be implemented at any point across the AI lifecycle to address technical challenges, threat landscape, and risk diversity.* They are:

Establish a cross-collaboration mindset

AI measures should include input from DevOps, SecOps, and legal teams to ensure they meet all organizational and compliance requirements. This includes creating a cross-team AI steering committee that meets regularly to discuss new potential issues and objectives; generating a responsibility flowchart or matrix to delegate response ownership and specific maintenance tasks; and establishing a common glossary around risk and incident severity. DigitalOcean’s GradientTM AI Platform can help with content summarization and storage for these documents, making it easy for all involved shareholders to access AI security glossaries, objective outlines, and executive briefings. Together, taking these steps can lead to a more robust security state at the starting point for any team’s security plan.

Define organizational security requirements

Not all organizations need the same AI security setup, and risks will depend on industry, assets, and compliance standards. Establish internal AI usage policies for employees across your organization, identify low-, medium-, and high-risk use cases (e.g., general AI use versus AI use with proprietary company data), and specify the guardrails each use case requires. Determine organizational readiness by mapping alignment to both security laws (such as the EU AI Act) and technical frameworks (from NIST), and figure out potential security gaps. Additionally, define where AI data is stored, whether it is in a public cloud, private cloud, or on-premise. With DigitalOcean’s Gradient AI Platform, developers can run an application consisting of regulatory and other laws to assist in evaluating overall compliance and regulation adherence.

Create proactive evaluation workflows

Getting ahead of attacks means maintaining continuous AI governance through testing, monitoring, and alerts. Part of this testing involves mimicking AI security risks, such as prompt injection, model theft, model inversion, and guardrail jailbreaking, to identify weak spots. Organizations should also use network monitoring and SaaS app logs to regularly scan for shadow AI, or the unsanctioned employee use of AI tools, which can create potential attack vectors or backdoors. When it comes specifically to data, teams must continuously train data and track model drift or irregular outputs that might indicate adversarial behavior. They should also establish role-based access controls to relevant data to avoid unauthorized access to sensitive information within the organization. This is streamlined within the DigitalOcean Gradient AI Platform, which includes capabilities for data exploration, KPI monitoring, and fraud detection.

Identify possible lower-risk vendors and models

To reduce the risk of certain injection attacks and malware-ridden models, identify which AI vendors and models are safe for employees to use in their workflows. These security requirements should encompass data encryption and data handling, access control, and adherence to industry standards, including certifications. During the evaluation process, top questions to ask any potential vendor include:

• What tools and external services can the agent access by default?

• Can those permissions be scoped or restricted per use case?

• Does the agent operate on least-privilege principles, or does it request broad access upfront?

• What happens when an agent receives a prompt injection via a retrieved document — does it have guardrails against unauthorized tool calls?

• Is there an audit log of every tool call the agent makes?

Be sure to create a scanning process for open-source models to preemptively identify malware or backdoor code before it is installed in AI systems.

Adopt Human-in-the-Loop workflows

Require human review for critical AI decisions, such as automated actions involving external data transfers or system changes. This reduces the risk of data transfer or system update errors that could introduce malicious code. Developers can also implement AI output confidence score thresholds or additional approval buttons for specific tasks, requiring human intervention. Beyond having humans involved in AI system workflows, be sure to train organizational staff about AI best practices, what attacks might look like, and when to escalate potential risks to security developers.

Protect data and sensitive information

Use data encryption and sanitization processes to make it difficult for threat actors to access and distribute data from AI training data and established models. This includes having processes for data anonymization and sanitation, so it removes any personally identifiable information (PII) before being used within an AI model. Organizations can also implement privacy-enhancing technologies designed to support differential privacy or homomorphic encryption. Lastly, review data retention policies to avoid storing data for prolonged periods or beyond the AI system’s use.

AI security FAQ

What is the biggest AI security threat in 2026?

The commercialization and productization of “agentic” AI threats, in which autonomous agents are manipulated into behaving in unintended or malicious ways, represent the primary concern for 2026. DigitalOcean research highlights that while agentic adoption is rising, 34% of companies cite managing security across these complex AI tools as a top challenge. Organizations must move toward inference-focused security that monitors for anomalous behavioral patterns at machine speed.

How is AI security different from traditional cybersecurity?

Traditional cybersecurity focuses on deterministic, rule-based defenses and static signatures to block known threats, whereas AI security must account for the non-deterministic nature of large language models. A defense-in-depth approach for AI should include specific input and output guardrails to manage unpredictability. Unlike traditional apps, AI security must protect the model’s context and the integrity of the training data, rather than just the network perimeter.

What are prompt injection attacks?

Prompt injection occurs when a user or external data source provides malicious instructions that trick an AI into ignoring its original system prompts to perform unauthorized actions. Agents have features for implementing guardrails and keyword filters to catch phrases like “ignore previous instructions” before they reach the model. These attacks are particularly dangerous because they exploit the fundamental way LLMs process natural language as both data and code.

What is data poisoning and why is it dangerous?

Data poisoning is an adversarial attack where small amounts of malicious data are introduced into a training or inference dataset to subtly degrade model performance or create backdoors. It is dangerous because it corrupts the model’s learned representations, causing it to systematically misclassify information or provide biased results that are difficult to detect. This can compromise the entire decision-making foundation of an AI-native business.

What are AI agents and why are they a security risk?

AI agents are autonomous systems that can call APIs, run code, and take actions without direct human intervention to achieve complex goals. They pose a security risk because unchecked agentic loops can result in unbounded API costs, data exfiltration, or the execution of dangerous tool calls if the agent is compromised by a prompt injection.

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